Such a correction method for truncation artifacts can be deployed with an x-ray diagnostic facility for angiography as known from US 2006/0120507 A1, which is shown by way of example in FIG. 1. The x-ray diagnostic facility has a C-arm 2 supported in a rotatable manner on a stand 1, at the end of which C-arm 2 an x-ray radiation source, for example an x-ray emitter 3, and an x-ray image detector 4 are positioned.
The x-ray image detector 4 can be a rectangular or square, flat semiconductor detector, which is preferably made of amorphous silicon (aSi).
In the beam path of the x-ray radiation source 3 is a patient support table 5 for recording for example the heart of a patient to be examined. Connected to the x-ray diagnostic facility is an image system 6, which receives and processes the image signals from the x-ray image detector 4. The x-ray images can then be viewed on a monitor 7.
The movable components 2 to 5 can also be supported individually or in a common manner on robot arms.
To create 3D data sets the C-arm 2, which is supported in a rotatable manner, is rotated with the x-ray emitter 3 and x-ray image detector 4 in such a manner that, as shown schematically in the top view of the axis of rotation in FIG. 2, the x-ray radiation source 3 moves on one circumferential path 7 and the x-ray image detector 4 moves on one circumferential path 8 around an examination object 9. The circumferential paths 7 and 8 can be traveled wholly or partially to create a 3D data set.
The examination object 9 can be for example an animal or human body or even a phantom body.
The x-ray radiation source 3 emits an x-ray beam bundle 6, which leaves a beam focus of the x-ray radiation source 3 and strikes the x-ray image detector 4.
The x-ray radiation source 2 and the x-ray image detector 4 move respectively around the examination object 9 in such a manner that the x-ray radiation source 2 and the x-ray image detector 4 are located facing each other on opposite sides of the examination object 9.
A significant processing step for the 3D reconstruction by means of filtered backprojection (FBP) is the filtering of the projection data along predefined lines in the x-ray image detector. The non-local nature of the filter core, for example the ramp filter or Hilbert filter, means that the filter lines have to run through the entire projection of the examination region and cannot be cut off, even if only part of the body region, for example the so-called region of interest (ROI), is to be reconstructed. The limited detector width however results in cut off projections of the examination region in many recordings, in particular when using the above-mentioned C-arm system, as this cannot be covered completely by the field of view (FoV). This results in cut-off filter lines in these projections. This produces pronounced reconstruction artifacts, which falsify the result and hinder, complicate or render impossible its qualified diagnosis. One example of this is an examination of the abdomen or thorax. A distinction can be made between two types of truncation:
(1) transaxial truncation and
(2) axial truncation.
Transaxial truncation is produced by examination objects that are cut off along the horizontal detector axis.
Axial truncation is produced by examination objects that are cut off along the vertical detector axis. Therefore in the case of the Feldkamp algorithm described in [1], with which filtering operates along horizontal lines in the x-ray image detector, only transaxial truncation of the filter lines is possible. However the development of new approximative and exact reconstruction algorithms and the use of novel scanning paths, such as circle and line, circle and arc, saddle, means that non-horizontal filter lines have also been introduced, as described for example in Pack et al. [2] and [6], Katsevich [3] and [4] as well as Nett et al. [5]. This means that both transaxial truncation and axial truncation can occur (see also FIG. 3). Such algorithms therefore require a new method, which is able to correct both types of truncation effectively. Since the algorithms promise a very high image quality, the solution to the truncation problem would be an important and central contribution to the resolution of reconstruction problems in computed tomography.
FIG. 3 shows possible truncations for non-horizontal filter lines by way of example. The contours of the examination object are mapped on the x-ray image detector 4. One filter line F1 is cut off transaxially on both sides. One filter line F2 is cut off axially on both sides. One filter line F3 is cut off axially on the left and transaxially on the right. One filter line F4 has no truncation. Truncation always occurs when a filter line exits from the x-ray image detector 4 before it exits from the examination object 9. Significant reconstruction artifacts result for every point on a truncated filter line. This can be the case for the majority of points in the case of non-horizontal filter lines.
In the case of C-arm systems, until now 3D reconstruction was carried out using the Feldkamp algorithm, which manages with a planar, circular scanning path. It uses only horizontal filter lines, so that only transaxial filter line truncation can occur. A hybrid solution has proven very effective for correcting transaxial truncation. Hybrid correction is made up of the so-called water cylinder correction and a Gaussian extrapolation, as described by way of example in Hsieh et al. [7], Zellerhoffet al. [8] or Scholz [9]. The method is implemented row by row. It is first checked in each instance using a threshold value whether truncation is present. If so, either water cylinder correction or Gaussian extrapolation is used, depending on the gradient of the (truncated) projection profile at the edge of the detector row in question (see FIG. 4). If the gradient at the left detector edge is positive or the gradient at the right detector edge is negative, water cylinder correction is deployed (see FIG. 5). With water cylinder correction it is assumed that the examination object can be approximated very closely by a water cylinder. To this end the center point and radius of the water cylinder are first determined. The missing projection values are then generated artificially by computer-simulated x-ray beams, which pass through the water cylinder. The detector row is continued with the projection values thus generated. If the gradient at the left detector edge 35 is negative or the gradient at the right detector edge 36 is positive, Gaussian extrapolation is deployed (see FIG. 6). With Gaussian extrapolation the missing projection values are approximated by a Gaussian curve. This produces the absent projection values, as with water cylinder correction.
A Feldkamp based reconstruction algorithm is also used with CT systems. However filtering takes place along non-horizontal lines in the x-ray image detector 4, with the gradients of the filter lines having very low values. With CT systems transaxial truncation cannot take place due to the size of the detector. Therefore only axial truncation has to be dealt with. To this end the x-ray image detector 4 is constantly continued in the axial direction, by repeatedly copying and adding the first and/or last detector row (see FIG. 7) as for example with Flohr et al. [10] or Kachelrieβ et al. [11].
FIG. 4 shows a projection profile p(u) along a cut off detector row. Either water cylinder correction (a) or Gaussian extrapolation (b) is used depending on the gradient of the measured projection values 11 at the edge of the row.
FIG. 5 shows an example of a water cylinder correction for the right detector edge 36. The missing projection values are generated by computer-simulated x-ray beams by means of a water cylinder 12 and used as artificially generated projection values 13 to continue the profile.
FIG. 6 shows an example of Gaussian extrapolation for the right detector edge 36. The missing projection values are approximated by a Gaussian curve 14 and used as artificially generated projection values 15 to continue the profile.
With CT systems truncation correction is carried out by constant axial continuation 16 of the x-ray image detector 4 in the axial direction, as shown in FIG. 7. The sizes of the extension regions are selected here in such a manner that no further filter lines are cut off.
[12] and DE 103 45 704 A1 and U.S. Pat. No. 5,640,436 do not describe any truncation corrections in the axial direction. Roughly speaking the patient can be seen as a cylinder of almost infinite length. With truncation in the transaxial direction a part close to the edge of the object is missing. Corrections try to estimate the edge of the object and to extrapolate the data. This truncation correction is well known in the literature.
With truncation in the axial direction the majority of the object is essentially missing. Close object edges are not present, being estimated and extrapolated. Such extrapolation methods for correcting axial truncation are not known.
DE 103 45 704 A1 and U.S. Pat. No. 5,640,436 deal only with transaxial truncation.
[12] describes the “long object problem”, in other words axial truncation, where iterative methods are examined. Axial truncation is therefore not corrected by data extrapolation but by appropriate selection of data in the reprojection and correction of the intermediate result. Section 2.D deals with an extrapolation method, which supplements missing data (see FIG. 5, mask 1). The missing data at the start and end of the scanning path is the result of data sorting from fan-beam to parallel-beam geometry regardless of the shape of the object and the size of the detector. The resulting truncation problem is however equivalent to transaxial truncation and is corrected accordingly.